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Tutorial I, by S. Sarkar

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Lecture 32 : Neural Network and Backpropagation Algorithm, by S. Sarkar

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Lecture 01: Introduction, by S. Sarkar

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Lecture 15: Collaborative Filtering, by S. Sarkar

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Lecture 44: Python Exercise on Kmeans Clustering, by S. Sarkar

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Lecture 42 : Kmeans Clustering, by S. Sarkar

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Lecture 38: VC Dimension, by S. Sarkar

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Lecture 12: K-Nearest Neighbour, by S. Sarkar

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Lecture 33: Deep Neural Network, by S. Sarkar

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Lecture 04: Evaluation and Cross-Validation, by S. Sarkar

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Lecture 37: Sample Complexity : Finite Hypothesis Space, by S. Sarkar

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Lecture 39 : Introduction to Ensembles, by S. Sarkar

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Lecture 43: Agglomerative Hierarchical Clustering, by S. Sarkar

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Lecture 40 : Bagging and Boosting, by S. Sarkar

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Tutorial II, by S. Sarkar

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Lecture 9: Python Exercise on Decision Tree and Linear Regression, by S. Sarkar

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Lecture 13: Feature Selection, by S. Sarkar

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Lecture 34: Python Exercise on Neural Network, by S. Sarkar

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Lecture 31: Multilayer Neural Network, by S. Sarkar

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Lecture 24: Introduction Support Vector Machine, by S. Sarkar

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Lecture 27: Nonlinear SVM and Kernel Function, by S. Sarkar

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Lecture 14: Feature Extraction, by S. Sarkar

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Lecture 06 : Introduction to Decision Trees, by S. Sarkar

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Lecture 02: Different Types of Learning, by S. Sarkar

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Lecture 18: Bayesian Learning, by S. Sarkar

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Lecture 03: Hypothesis Space and Inductive Bias, by S. Sarkar

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Lecture 41 : Introduction to Clustering, by S. Sarkar

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Lecture 23 : Logistic Regression, by S. Sarkar

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Lecture 35: Tutorial VI, by S. Sarkar

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Lecture 05 : Linear Regression, by S. Sarkar

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